{"id":"W2800997190","doi":"10.1186/s12920-018-0342-1","title":"EAGLE: Explicit Alternative Genome Likelihood Evaluator","year":2018,"lang":"en","type":"article","venue":"BMC Medical Genomics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Core Research for Evolutional Science and Technology; Japan Society for the Promotion of Science; National Institute of Advanced Industrial Science and Technology","keywords":"Computational biology; Human genetics; Biology; Genome; Genome Biology; Genetics; Evolutionary biology; Genomics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005139234,0.0002366438,0.0002384547,0.00004020822,0.0001682995,0.00002473529,0.0005349417,0.0002205334,0.0004111256],"category_scores_gemma":[0.0004007455,0.0002178923,0.0001286549,0.00007751325,0.0002587492,7.812697e-7,0.0004760538,0.0001126829,0.0002749823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003448009,"about_ca_system_score_gemma":0.0006817398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002888185,"about_ca_topic_score_gemma":0.0001891237,"domain_scores_codex":[0.9982051,0.00008789702,0.0003464748,0.0005451046,0.0003486404,0.0004668401],"domain_scores_gemma":[0.9988014,0.00004388213,0.0001031608,0.0004972199,0.0001987831,0.000355582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003462308,0.0002658596,0.007866126,0.00004684693,0.0005016726,0.000008712774,0.0008952886,0.00004287481,0.9659971,0.0008650885,0.006974633,0.01618957],"study_design_scores_gemma":[0.002637458,0.001573064,0.01588874,0.00002576724,0.00009613419,0.00006891353,0.0004081247,0.001519663,0.09640066,0.001806482,0.8786356,0.0009393828],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854355,0.002136119,0.006584206,0.0001568329,0.0006728428,0.0002509653,0.00004288469,0.000008818321,0.004711855],"genre_scores_gemma":[0.9885514,0.001552619,0.003422671,0.001216397,0.004047805,0.0000483618,0.00004967949,0.00005047225,0.001060626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.871661,"threshold_uncertainty_score":0.888539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958887604775311,"score_gpt":0.2800712954907516,"score_spread":0.2604824194429985,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}